Current defectivity inspection technology is very complicated and takes several steps. For instance, an incoming wafer first undergoes an optical beam inspection. The optical beam inspection detects by capturing images of wafers using different wavelengths of light. Select defects (identified via the optical beam inspection) are then imaged using, e.g., top-down scanning electron microscope (SEM) imaging. From the images, the defects are then classified. This classification is usually done by eye by a user visually inspecting the results. The results are then stored for future reference. As such, the current inspection technology is extremely time consuming, and has a low throughput.
Further, the optical inspection is likely to fail in detecting defects on heavily defective wafers. For instance, defects are detected based on regions appearing differently in the images from one die/chip to another on the same wafer, i.e., the differing regions are the defects. However, with a high defect density the same defects can occur from die to die on the same wafer, and thereby escape detection.
The top-down SEM images may also fail to detect all of the defects identified by the optical inspection due to wafer misalignment. Specifically, misalignment can result in the SEM capturing images of the wafer offset from the regions where the optical inspection found defects.
Further, the optical inspection may be able to detect buried defects. However, because the defects are buried, the top-down SEM images may not be able to image them. Thus, these defects would not be identified through the process.
Accordingly, improved defectivity inspection techniques that are less time-consuming and less labor-intensive, increase throughput, and which are adaptive to detecting new defects as they arise would be desirable.